{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows multiple outputs from a single jupyter notebook cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "pd.__version__  # for the record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(252, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>UpperB</th>\n",
       "      <th>LowerB</th>\n",
       "      <th>PercentB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>132.089996</td>\n",
       "      <td>134.100006</td>\n",
       "      <td>131.779999</td>\n",
       "      <td>133.919998</td>\n",
       "      <td>117.161659</td>\n",
       "      <td>202385700</td>\n",
       "      <td>132.373927</td>\n",
       "      <td>125.316073</td>\n",
       "      <td>1.219057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-05</th>\n",
       "      <td>133.779999</td>\n",
       "      <td>134.080002</td>\n",
       "      <td>133.389999</td>\n",
       "      <td>133.809998</td>\n",
       "      <td>117.065437</td>\n",
       "      <td>165936000</td>\n",
       "      <td>133.254297</td>\n",
       "      <td>124.912703</td>\n",
       "      <td>1.066618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2011-07-01  132.089996  134.100006  131.779999  133.919998  117.161659   \n",
       "2011-07-05  133.779999  134.080002  133.389999  133.809998  117.065437   \n",
       "\n",
       "               Volume      UpperB      LowerB  PercentB  \n",
       "Date                                                     \n",
       "2011-07-01  202385700  132.373927  125.316073  1.219057  \n",
       "2011-07-05  165936000  133.254297  124.912703  1.066618  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>UpperB</th>\n",
       "      <th>LowerB</th>\n",
       "      <th>PercentB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-06-28</th>\n",
       "      <td>132.289993</td>\n",
       "      <td>132.990005</td>\n",
       "      <td>131.279999</td>\n",
       "      <td>132.789993</td>\n",
       "      <td>118.641281</td>\n",
       "      <td>169242100</td>\n",
       "      <td>136.500761</td>\n",
       "      <td>128.219241</td>\n",
       "      <td>0.551922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-29</th>\n",
       "      <td>135.199997</td>\n",
       "      <td>136.270004</td>\n",
       "      <td>134.850006</td>\n",
       "      <td>136.100006</td>\n",
       "      <td>121.598610</td>\n",
       "      <td>212250900</td>\n",
       "      <td>136.721010</td>\n",
       "      <td>128.792993</td>\n",
       "      <td>0.921670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2012-06-28  132.289993  132.990005  131.279999  132.789993  118.641281   \n",
       "2012-06-29  135.199997  136.270004  134.850006  136.100006  121.598610   \n",
       "\n",
       "               Volume      UpperB      LowerB  PercentB  \n",
       "Date                                                     \n",
       "2012-06-28  169242100  136.500761  128.219241  0.551922  \n",
       "2012-06-29  212250900  136.721010  128.792993  0.921670  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/SPY_20110701_20120630_Bollinger.csv',index_col=0,parse_dates=True)\n",
    "df.shape\n",
    "df.head(2)\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib qt\n",
    "import mplfinance as mpf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1,2,3])\n",
    "x\n",
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tdf = df.iloc[0:24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mymarkers = ['$1$',None,'$2$',None,'$3$',None,'$4$',None,'$5$',None,\n",
    "             '$6$',None,'$7$',None,'$8$',None,'$9$',None,'D',\n",
    "             None,'<',None,'x',None]\n",
    "len(mymarkers)\n",
    "len(tdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color=['c','g','r','b','y']*5\n",
    "color=color[0:24]\n",
    "ap = mpf.make_addplot(0.99*tdf['Low'],type='scatter',marker=mymarkers,markersize=45,color=color)\n",
    "mpf.plot(tdf,type='candle',addplot=ap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ap = mpf.make_addplot(0.99*tdf['Low'],type='scatter',marker=mymarkers[0],markersize=45,color=color[0])\n",
    "mpf.plot(tdf,type='candle',addplot=ap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9riPUgy7gC0IfALQARh9wPRC9kXysBPAOoQ8AEHD19UYeyT6kh2ZN0/LVa5XS3buQFqofKwGCgdAHhKH8vFxt2ZyhCXekyZbQKdjlIEzUFdBMpqrv3Xv00hWphDQgUBicGQiA/LxcLVy4UPl5ucEuxaP8/DytXvGc8vPzgl0KIJstQQsWLOBmKiDA6OkDAiA/P09PP/20Bl19reJt9KShtqM52R4vdZ45c1ofbH9HY341Vpdc0qHGupb6WTRbQictXLhQR07Y5XIFuxqg5SL0IaAYDgWo7WhOtn5xzVX1bvPHVzd6XL5jd1aLDH4AAo/Qh4BiOBSgtuoevrZt28liadzbsMNRqXPnznrsHURN/GcT8IzQBwBBYrFEKDIyMthltDj8ZxPwjBs5AAAAwgChDwAAIAwQ+gDAR/l5uVq5fKlhh+YBgAsR+gDAR4x3CCCUcCMH0AR1jbV2JPuQpKo5QC8ed8yXsdZCYQaNUKixKTy1dfbhQzW+X6yljqsHIDQR+gAfNWastYdmTfO43Nux1qp7lK67/gbDBqpQqNFXDbX1vNme21liXD0AxkHoA3xU71hrJsliNsnhdEkX9PQx1lpoqm6vjZs2qU+fvo3a5+DBA5o8aVK9be1wVDa6Bm+2BQBPCH1AE3kca+186DNfFPoQ2vr06atBgwY1+TgxMTGSpHPnzvq8b2P5a6Bip7NQRedeVXnZP+WozJPF0lFR1msUHfsfMpkYaxAIBYQ+AGhm3ZK7a8fuLI+9gNmHD2ne7GlatmqtuvfoVWOdL58R9NdAxcUF/5/sPzxSc1nhGyoueEvxia/LZLI0+TGMgDmR0ZIR+gAgCBoKCd179FJqv/7NVE3DzJZ2kqSIyJ6KiExWedk+OR35KivZpbKSv6m1dWSQK2w65kRGS0foAwA0qE30zUpKOSGz2SpJKi58R6dzJ0uSHJXHg1ma3zAnMlo6Qh8AoEEmk0kul0PfH7taTscPcjpPu9dFtRkexMr8jzmR0VIR+gAAjeJ0nFFlxde1lpstHYNQDQBvMSMHABiIv+62DQRLxKWK65iutu0fVlTrEe7lRedeCWJVABqLnj4AMBB/3W0bCCaTSbFx0yVJTsdZncjpJkmqKD8YxKoANBahD2HL5apQZcWRWssjIpNlMrUKQkWAcdl/eFKFZzcopt09MpvjVFryiXtdZKseQawMQGMR+hC2zp5epAL772stj2k3Re3jnw9CRYBxlZd9KZerSAX21TWWmy3xio69I0hVAfCGYT/Tl5GRodTUVM2dO7fWul27dmnixIm68sorNXDgQN1111367LPPamxz4sQJzZs3T9dcc40GDRqkW2+9VR988EFzlY8Q4HQWeFzuclU0cyWA8bW95GG1tl4vs7m9ZGotS8Slim47VZ26fuweww+AsRmup89ut2v+/Pnav3+/oqKiaq3fsWOHZs6cqenTp2vJkiUqLi7W8uXLNWXKFL311lvq2bOnzp49qzvvvFMdOnTQihUr1L59e73yyiuaPXu21q9fr5EjQ38QUfiPydRalySsOz/+mEmRUQOCXZKh1TVjQfbhQzW+X4xZC0Jb6zYj1LrNiIY3BGBYhgt927ZtU3FxsbZu3arbbrut1vr33ntPw4cP14MPPuhetmTJEo0aNUq7d+9Wz5499d577+nkyZPKzMxUly5dJElPPPGEdu7cqS1btoRs6Dt48ECtZREWsyodzkZtC89crlKdzr1LZotNMW3TWtyYY55Cmq8BrTEzFsybPa3OdcxaAADBY7jQN2rUKE2YMEEWi+d5HFesWFFrmclkkiRFRFSdzq233qrrrrtOCQk/DXlgNpsVHx+v4uLiAFQdWNUTrE+eNMnnfeGBq7LGj05Hvs79+FtVVn6rDgkvB6ko/2oopHkb0HyZsUBi1gIAMALDhb6uXbt6tX1ubq6WLFmipKQkjR07VpLUqlWrGoFPkk6ePKmDBw/q3nvv9VutzaWuydmPZB/SQ7OmafnqtUrp3qvWflxOq19r62g5HKfV2vpzSQ6d+3GVnI48FRf8UXEdnpIlonOwS2yyQE0rxYwFABB6DBf6Guujjz7SnDlzVFZWphEjRmjLli1q3769x23Lyso0b948xcXFafLkyfUe12Sq+jKa5JTa4c18/jacHj176YpU40zMLknmM6cV8+5bavXVlzKfPStHQicV/eomlQ41zqXT6LbjFd12vPtnszlGZ/LnSHKpovxzRUTWH/qqXycOR6Xk4TXjNJvkcLpqLHM4Kt37evM6q97W1/0sEV6EtIq6H8v9s0kez7nuQuo+ZnO68Hn0ZT9fH9OI7ylG4mu7BKoOr17fBnltB4JR2gU1+fr3QArh0Dd06FBt3bpVx48f15o1azRx4kRlZmYqMTGxxnaFhYW6//779c0332jDhg11BsNqyYlxio6ODmTpfmPPi5UkJcXHKiUpLsjVXGTpk9Lvaw6H0m79/ystXCgtWBCcmi7yww/vKioqUTExgyQ59XXBPve6Lgld1a5d/c+po7jqtXbu3FmvH7tv90Sv2qy6rbvYvGvr6v0sZpMs5sa9OzjPb+fpsXw5XkPHDIbkRO9qqD7vCItZEZbGDXpQvZ1RzjkUeNsu/ubv35eWItjtAs+SE+NUVOTdFZeQDX1Wq1UpKSlKSUnR4MGDNXr0aK1du1YLFy50b5Ofn6+pU6fqzJkzeuWVV9SnT58Gj5tz0i6rNTSG7DhxqsD9Pe6EPcjV1HSJ06I4SRWXJcvRoaOivviXTBXlcj39tI79P5PkbCB8N4dvv6n6OIDJZJXklMtVKkmyWBL1Q0FvnS6s/zm1WOO1Y3eWiopqXwY9cviQ5s6aphWr1yqlR81L79HRMbJY43XEizY7nl/g/u5NW1fv53C6ZL6o17Eu1b2Tnh7Ll+M1dMzmZDJVvVHmnLTL1fjy3edd6XB6vHHKk+rtgn3OocDXdrnYP/L26rFP5qjyos/rXnfpL/Xo0Gca3N/fvy+hzl/tAv+6sF2Kioq82jekQp/T6dSuXbuUlJSkvn37upe3adNGXbt2VXZ2tnuZ3W5XWlqaXC6X/vjHP9bqAayLy6WQeXFX12nEmk/Pe0w/Tp8tZ/tLJEntX/itLlmWLpPLJcuJ43LEBT/0mUxWuVzFcrl+urnHZG6rDp3WSbI06jlt6DOTKT08X3r3tr18bevqbR2VlVIj96u+BO3psdw/u9To47m3r+OYweDr89gcjxXOmvpcffHDPn1jrz0l3K5vP9D8IQ2HPp9e3wZ7bQdCSz63UOZLu4RU6DObzVq6dKmSk5O1bt069/LS0lIdO3bMPRSLy+XS7NmzVVlZqS1btqhDhw7BKjl8tWoly3enlfibm2QuLFDk8e8kSY72l6gixRhTNnW67H9VVvyxKiuOyCWHWrXqp9bRv5DZHBvs0vym+u5tXy5BB/vO7/y8XG3ZnKEJd6TJltApqLUg9IztfqtaW6ySpCGdjfNZYiCYDBf67Ha7KiqqLq86HA6VlZXp1KlTkqTY2Fg98MADmj9/vpYvX66bb75Z5eXleumll1RQUKCJEydKkrZv367PPvtML774opxOp3v/avHx8c17UmEq8ptDijr4VY1lFZd2k6tNmyBVVFNERKIi2k4IdhkBVded39mHD2ne7Glatmqtuvcw5p3f+fl5Wr3iOV13/Q2EPnjtmx8PaoDtKv0qebyGJf5bsMsBDMFwoW/WrFnau3ev++fc3Fzt3LlTkpSenq7x46vutty4caM2bNig6Oho9e7dW5s2bdKVV14pSdqzZ48k6YEHHvD4GF9//XUgT6HZ2GwJWrBggWy2hIY3DoLSocP14wMPylxYKOuH7yvy+xNq/a9/KOr/9qrsyiHBLi9s1BfeuvfopdR+xrrzG/CHA2e+1IEzX2rLwQxN6zdH/zVkYbBLqoGebASD4UJfZmZmg9uMGzdO48aNq3N9enq60tPT/VmWIdkSOmnhwoU6csKYH7J1tm+vM/Or7tRtfeMtSrrtRklS1FdfEvrqEK5TnOXn5So/P6/GsobO2WZL4I8lamgfVfUZ4t7tr1BMZIz2ncqSw+XQ2i9W6jd9Jqlb25QgV/gTerIRDIYLfWgZEu69U5HfHVPBuN9IrVopZusb7nUVl3YLXmHNxJde2HCe4mzL5gytXvGcx3V1nfOsuY9ozkPzA1gVQs2vU8br513/XbGt2kqSPjj6rh7YWTWT0a5v/0f3pM4IZnlA0BH6EBDRf35fJqdTUV99WWN5WZ/LVfJvPw9OUc3Il17YcJ7ibMIdabru+htqLGvoc4dG/VgDgqfCWaEoS2v3z21b/TS+XKUzNIbiAgKJ0IeAOJW+XO02rFWrb76WnE454hNUOO5W/fjAQz9NJQKPwnGKM1tCpzovcfG5QzTWPf9zqz7L3aNxPW5XlCVK/3P0Hfe64YmjglgZYAyEPgREwcTJKpg4uWoQIadTsliCXRKAFu7I2W8kSW8dfq3G8usv+7VSOw4IRkmAoRD6EFgmE4HPgGy2BM2a+wiXSNGiLP231Xr960wd/HG/yipLldyuh36dMk639roz2KUBhkDoA8KQLaETN0GgxRnV9XqN6np9sMsADIvQBxhM9TRogdoeABCeCH2AQTRlyrQL90fgHDx4ICDbAkBzIPQBBlHXlGmNEeqDMxtddaCePGmSz/s2JOqfWWq99++K+vJfMhcWquiXN6rgP+7w+vEAoC6EPsBACG6BlZ+Xq03rXtAvx96ueFvjZ0GoK5CfOXNaH2x/R2N+NVaXXNKh1n6NDeOW3O/VZWzNz6KZC84R+gD4FaEPQNjIz8/T008/rUFXX+tV6JPqDuTXjBrtj9IAIOAYJRcAgszRqbO+e+8jHfv753J46DEEAH+gpw8ADKD8Z1WDB7tMpiBXAqClIvQBaHGO5mR7vCHmSPYhSVXz+l48JzI3wwBo6Qh9AFqUoznZ+sU1V9W7zUOzpnlcvmN3FsEPQItF6APQolT38LVt204Wy0VvcSbJYjbJ4XRJF/T0ORyVOnfurE/D5QBAqCD0AWiRLJYIRUZG1lx4PvSZLwp9RmDd+YEic47IXFIiSYr4/qTa/eFllQ4YpLKrhga5OgAtAaEPgNeYKs7/EmZMkbm4yP1z5LdH1fHpx1R2xc90/H8+DmJlCFe+jmsJ4yL0AWg0pooLnMqkLmr1zde1lld0vTQI1QBNG9cSxkToA9Bo9U0Vl334kObNnqZlq9aqe49etdZzd2z9vtv5N8nprL3CzHCqCCxf7naX+J0ORYQ+AF5p6E2+e49eSu3Xv5mqaUFMJsliCXYVCDNNudtd4o73UEPoA2BINluCZs19RDZbQrBLAVosX+52l7jjPVQR+gAYki2hk+Y8ND/YZQBhIdTudodvCH0hqNjp0KtnT2tfaZEOfVemogqH/qtjZ90ce0mwS0MYo2cOAIyN0BeCPi0p1NLTJ2ss+6K0mNCHoDJaz5zHYWJMkrOOwZmBat68HhqzracbJbIPH6rx/WLcJIFAIPSFoDhz1Ye9Y81mFXi62w8IY00ZVoYhZcJbIF47Dd0oMW82N0mg+RD6QtBVbWL0r5R+KpdLg498GexyAEOpb1iZI9mH9NCsaVq+eq1SutccVoaeFfg6JFF9r516b5Sog683SeTn5WrL5gxNuCNNtgTG1UNthL4QFW22qMLJJSnAk7r+AJtMVd+79+ilK1IZVga1BWpIIo83SvhZfn6eVq94TtddfwOhDx4x6icAAEAYIPQBANAA7k5HS8DlXQAAGmC0u9MBXxD6QlCly6XfnT6pUxcMFbCnpFDz877VuLaXaGgb7kAEAAA1EfpC0MnKcq23n6qx7HB5qQ6Xl8olEfpakB9K8lVYXlBreVREa3WOTgpCRQCAUEXoC0EdLREaEGXVGWelIi1mVTickqvqzsSr2kQHuzz4yZnS0xq+pa+cLs9jMb5988e6ouPPmrkqAL7w94DPgC8MG/oyMjL0/PPP6/rrr9eKFStqrNu1a5f+8Ic/6Ouvv5bT6VRqaqpmzpypoUOHurfJzc3V0qVL9de//lVlZWVKTU3Vf/7nf2rgwIHNfSp+ZzVb9GbXXjKZpJSkOB05YZeLeRFbnEpnRZ2BT5KiW9GjCxgdg4XDSAwX+t11kRAAACAASURBVOx2u+bPn6/9+/crKiqq1vodO3Zo5syZmj59upYsWaLi4mItX75cU6ZM0VtvvaWePXuqvLxcd999t6xWq9avX6+oqCht2rRJ99xzj9555x117do1CGcGeMdm7aQtv35P9jK7JMledkaPfjJLkjTQNljd2qYEszwAjVDXgM/1DfYsMVg4AsNwoW/btm0qLi7W1q1bddttt9Va/95772n48OF68MEH3cuWLFmiUaNGaffu3erZs6e2b9+uI0eO6P3331dKStUfxqefflp79uzRunXrtGjRomY7H6ApBnca7v73xv1r3P++vXdaEKoB4Iv6wpuvgz0DvjBc6Bs1apQmTJggi8Xicf3Fl3olyXR+mP2IiKrT+eSTT3TZZZe5A1/1uuHDh2v37t0BqBoILJfLpS0HN0iSWlva6JfJY4NcEQAg1Bgu9Hl76TU3N1dLlixRUlKSxo6t+kOYk5Pj8TiXXXaZ/vSnP6mkpERt2rTxeDyT6aepmoyuus5QqTec+Lttvjz9Lx22fy1JGtv9VsXweT6fXNgu/N4YRzi+lwXitejLMd3bmc5/1bmh55/5XWp+TXntGC70NdZHH32kOXPmqKysTCNGjNCWLVvUvn17SVJRUZG6dOlSa5/qD8UWFBTUGfqSE+MUHR1ad8AmJ8YFuwTUwV9t87t9f3T/+8GR9ysliTb3hT0vVpKUFB/Lc2hA4fRe1sbcSwsWLNDg/r3UubN/zrv69d3F1vjXd/U+FrNJFrPnBOFpufP8Mm8eC/6VnBinoiLv5nMO2dA3dOhQbd26VcePH9eaNWs0ceJEZWZmKjExsUnHzTlpl9Va4acqA8tkqmr0nJPcvWs0/myb0soSbdqXKUnq1jZFNlMfHTlh90OV4efEqQL39zieQ8MIz/eyNpo09UGVOOW33+fj+QXu7419fVfv43C6ZHbWfvItZpMcHpZXL/PmseAfF/6+FBUVebVvyIY+q9WqlJQUpaSkaPDgwRo9erTWrl2rhQsXKjY21uMTUVBQIJPJpLZt29Z5XJdLIfemE4o1hwt/tM37Oe+ouLLq9Xx7n7slmWhvH1U/b/zOGBPt0jS+vL7d27nOf13owg6+i9fxuxR0vjz35sCUEhhOp1M7duzQgQMHaixv06aNunbtquzsbElSSkqKjh07Vmv/o0ePKikpSa1bt26WegF/eCf7DUmS2WTRuB7/EeRqAAChKqRCn9ls1tKlS7V8+fIay0tLS3Xs2DHZbDZJ0s9//nN99913Onz4sHub8vJyffLJJ7r22mubtWagqf6j92SlXTFd6SNXqUOb+GCXAwAIUYa7vGu321VRUfWZOofDobKyMp06VTXPbGxsrB544AHNnz9fy5cv180336zy8nK99NJLKigo0MSJEyVJ//7v/66+ffvqv/7rv7Rw4ULFxMToxRdfVEVFhe69996gnRvgizHdbtKYbjcFuwwAQIgzXOibNWuW9u7d6/45NzdXO3fulCSlp6dr/PjxkqSNGzdqw4YNio6OVu/evbVp0yZdeeWVkqrG5PvDH/6g9PR0TZkyReXl5Ro4cKAyMzPVqVOn5j8pAACAIDNc6MvMzGxwm3HjxmncuHH1btOxY0ctW7bMX2UBAACEtJD6TB8AAAB8Y7iePgCh56zdpH/+w6IffzTpqsEOdb3UGeySPLLZErRgwQLZbAnBLgUAmh2hD0CT7NoRoQdnRqu46KdBvaZMK9X8J0oNNz2TLaGTFi5cqCMnwmkQYACowuVdAD47lW/SnAdqBj5JWr+2td5+y7vpgQAAgUXoA+CzTz6OUElxVeB79nfF+vs/zqpt26pLu396o1UwSwMAXITQB8Bnp0799BZy5VWV6hjv0mXJVaHvH1kRcjiCVRkA4GJ8pg+Azy7r9tMNG2teitLQqx364l9VbyulpSYVFpjULo4PzwGAERD6APjs2tEVSuri0InjFv3pjSj96Y2a680WAh/gb0dzslVYWFhrefbhQzW+XygmJkbdkrsHvDYYG6EPgM+iWkubthTp8Ufa6O+f1rxxo0NHp2JiglQY0EIdzcnWL665qt5t5s2e5nH5jt1ZBL8wR+gD0CRffmHRXWnlWri4RH/ZGamlS9pIkq4fU2G4IVuAUFfdw7dx0yb16dO3UfscPHhAkydN8tg7iPBC6APQJHNmRNdaFhHh0r33lQWhGiA89OnTV4MGDQp2GQgx3L0LoEn6D6is8XNKd4c2vlqobsnGnJUDAMIVPX0AmuSNtwt16GuzTp4w65IOLv2sv0Nm/jsJAIZD6APQJGaz1KevU3360rMHAEbG/8cBAADCAKEPAAAgDBD6AAAAwgCf6QMAoJlMvduq/V94/tO7bGWxho2o9LjuYgcPHmj0Y3qzLVo2Qh8AAM3k759GqqTY86jl331n1rAG9o85P83N5EmTvH7sGKbICXuEPgAAmsmjT5To0NcW989vvdlKRUUmRUS4dP2Yigb375bcXTt2Z9U59+682dO0bNVade/Rq8Y65t6FROgDAKDZTLyr3P3vnCNmvbIxSlLVtIXt27sadYyGwlv3Hr2U2q+/V3U5HB4uK5skp9kkh9MluRqxPQzPr6GvvLxcrVq18uchAQBokd74409/L38zobyeLQOn+pLvuXNnm7Q/QkOTQp/T6dSbb76pd999V1999ZVKSkr01VdfSZLmzZunRx55RDabzS+FAgDQUlRWSm+8VhX64m1ODR8ZnJ6z+i4XH8k+pIdmTdPy1WuV0r1XrfVcMg49Poe+0tJSTZ06VVlZWZIkl8slk6nqw6lFRUV677339I9//EOvv/664uPj/VMtAAAtwMcfRejHM1Wjpv3HxHJZLA3sEEB1Bbfzf9LVvUcvXZHq3eViGJPP4/S9/PLL+t///V9JUv/+/RUZGeleV1xcrKioKOXm5uqll15qepUAALQgf3w1yv3vW38TnEu7CD8+h77t27fLZDJp1apVeu211xQdHe1eFx8fr02bNsnlcunjjz/2S6EAALQEp/JN+suuqgttQ4dVqEtX5q1G8/A59OXm5ioqKkrXX3+9x/X9+/dXmzZtlJ+f73NxABAoX5YW696T2eqf/bnOcScimtHWP7WS01l17fT2ifTyofn4HPqio6NVVlZWZ6jbv3+/SkpKavQAAkCw/V9Bge49ka1bjh/Sx8UFKnI5VeCkpwXNp0MHpy7r5tDgoRWNGpuvJcnPy9XK5UuVn5cb7FLCks83cgwYMEB/+ctfNG3aNKWlpamysup/yrt379bBgwe1adMmmUwm9evXz2/FAoCvviwt1soz3+ujbwpU/Zn5xo2KBvjX+NsqNP628Ap71fLz87R6xXO67vobZEvoFOxywo7PoW/KlCnavXu3vv76az366KPu5ffdd5+kqrt5zWazpk6d2vQqAcBHX5YW64Uz3+svxT+FPUdQKwKA4PD58u7gwYO1dOlSWa1WuVyuWl9Wq1VLlizR0KFD/VkvADTKyYpy3Xuy6jLuJ8UFkgh7AMJbkwZnHjt2rEaNGqWdO3fq0KFDKioqUkxMjHr37q3rrrtOsbGx/qoTALyyu7hAfyHsAYBbk6dha9euncaPH++PWgDAb25v10HdIlvphTO5yiotkkWEPwDhzefLu5JUVlamF154QS+++GKN5XfffbcWL16sc+fO+XzsjIwMpaamau7cubXWZWVladKkSRoyZIiGDRumqVOn6sCBAzW2OXnypObNm6dRo0YpNTVVY8aM0ebNm+Vy8dFtIFxcbY3Va1166pXE7hrYumokgSBOfAAAQeVz6CspKdHtt9+uNWvWKDe35q3XP/74ozZv3qyJEyeqqKjIq+Pa7XZNnz5d69evV1RUVK31+/btU1pamjp37qzNmzdr7dq1KikpUVpamk6dOiWpakaQtLQ0HTt2TC+88IK2b9+utLQ0LV68WBs2bPD1lAGEqOrwtzmpu4a1bSuJ8Acg/Pgc+tavX68DBw7I5XKpTZs2NdYlJCTI5XIpOztba9as8eq427ZtU3FxsbZu3ap27drVWp+RkaHOnTsrPT1dPXv2VL9+/bR48WLZ7XZt375dUlVP4LFjx/TII49o4MCBuvTSSzVhwgSNGDFCW7du9fWUAYS4q62x+mTQIG1O+qnnDwDChc+h7+2335bJZNKTTz6pxx57rMa6NWvW6KmnnpLL5dIHH3zg1XFHjRqlDRs2qEOHDh7XP/vss3rttddkNv9UekJCgqSqHr4LXbiNJLVq1cqrWgC0TBde9p3VPkGdIyIb3gkAQpzPoe/7779XVFSU7rjjDo/rJ06cqFatWunkyZNeHbdr166yWOq+8GK1WmsFwl27dkmqGjBakq6++molJydr9erVOnPmjCTp008/1V//+ldNmDDBq3oAtFxXW2M1p0NnmU2mYJcCAAHn89270dHROnfunE6ePKnExMRa63NyclReXu7xEq0/HT9+XIsWLdLIkSM1bNgwSVU9ehs3btT999+v4cOHKyIiQg6HQw8++GCDoc9kqvoKBdV1hkq94YS2MSbaxZhoF/+48Hn0x3Pp7+MF6pjhpinPoc+h72c/+5k++eQT3X333Zo6daouv/xyxcTE6OzZs/r888+1fv16mUwmXXHFFb4+RIMOHz6se+65RzabTcuWLXMvLy0t1cyZMyVVXWru2LGj/va3v2n16tVq27ZtvcEvOTEu5OYLTk6MC3YJqANtY0y0izHRLk1jz6saG7eLLVYpSU1/LquPlxTvn+NdeEx/1RjOkhPjVFTk3UdTfA59d999t/bs2aNvv/1WTz75ZK31LpdLJpNJkydP9vUh6pWVlaUZM2aoR48eevnll2v0KL755pv6/PPP9Ze//EWdO3eWJF1xxRXKz8/Xb3/7W912222KiPB86jkn7bJaQ2NORJOpqtFzTtrFSDTGQtsYE+1iTLSLf5TLqtlzH1G5rDpywt7k4504VeD+HueH40nS8fwC93d/HTPcXPj74u0IKT6HvuHDh+uJJ57Q0qVLVVFROyRFRES4x8nzty+++EJTp07ViBEjtHz58lo3aGRnZys2NtYd+KolJyeruLhYp06dqrWumsulkHvTCcWawwVtY0y0izHRLk0Tb+uk2Q/Nl+Sf57H6GP5sl0AcM1z58hw2aUaOO+64Q9ddd53eeecd7d+/XwUFBYqOjlbv3r01duxYXXrppU05vEenT5/WfffdpxEjRmjlypUeb/pITExUQUGB8vLy3Hf2SlVhMDIyUvHx8X6vCwAAwMiaPA1bp06dNG3aNH/UIqlqcObqnkOHw6GysjL3oMuxsbFauXKlysvL9fDDD7vvzK0WGRmpuLg43XLLLVq7dq0eeughPfzww+rQoYP27t2rN998U+PHj6/z0i4AAGi6oznZKiwsrLU8+/ChGt8vFBMTo27J3QNeWzhrdPo5efKkLBaLu+fMm6FYPN3dW5dZs2Zp79697p9zc3O1c+dOSVJ6err27NmjgoICjRkzpta+Q4YMUWZmpuLj47Vx40atWLFCU6dOVUlJiTp37qx77rlH999/f6NrAQAA3jmak61fXHNVvdvMm+25s2jH7iyCXwA1OvSNHj1al1xyiT799FP3z6ZG3CtsMpn01VdfNbqgzMzMetePHz++Uce5/PLLtW7dukY/LgAAaLrqHr62bdvJYmlczHA4KnXu3FmPvYPwH6+uc7ou+sTgxT8DAABIksUSochIZrsxkkaHvltuuUUxMTE1fm5MTx8AAACCr9Ghb+nSpfX+DAAAAOPyee7duXPnas6cOVziBQAACAE+j12yZ88eVVZWcokXAIAWyGZL0IIFC2SzJTS8MUKCzz19N9xwg0pKStzDqQAAgJbDltBJCxculC2hU7BLgZ/43NM3duxY/fjjj5o7d66uvfZa9e/fX3FxcTKba+fIW265pUlFAgAAoGl8Dn133XWXpKphWz788EN9+OGHHrczmUyEPgAAgCDzOfRdeAMHN3MAAAAYm8+hLz093Z91AAAAIIB8Cn1ZWVnKycmR3W5Xly5ddMMNN6hr167+rg0AAAB+4nXoe+qpp/TGG2/UWLZq1So98cQTuv322/1WGAAAAPzHqyFbtm/frtdff10ul6vGV2VlpZ555hl9/vnngaoTAAAATeBV6HvzzTclSZ06ddKCBQu0Zs0aPfTQQ2rXrp2cTqdeeeWVgBQJAACApvHq8u7+/ftlMpm0atUq/exnP5MkjRo1Sr169dL06dPp6QMAADAor3r6CgoK1Lp1a3fgq3b11Ve71wMAAMB4vAp9TqdTrVu3rrW8epnT6fRPVQAAAPArn+feBQAAQOjwesiWyspKZWVleZyFo651gwcP9r1CAAAANJnXoa+wsNA97+6FTCaTx3Umk0lfffWV7xUCAACgybwOfcyzCwAAEHq8Cn3jxo0LVB0AAAAIIK9CX3p6eqDqAAAAQABx9y4AAEAYIPQBAACEAUIfAABAGCD0AQAAhAFCHwAAQBjwepw+AADQshU5HdpbUqgPThQpvkQa2Do62CXBDwh9AADA7fPSYt33/RGdclS6l41oE6OXOicr2mwJYmVoKi7vAgAASVKFy6VZuUfdga86JPy1pFArTn8fvMLgF4Q+AAAgSfq6rEQnKsslSdPa21R6zTXqH2WVJL1V8CNTsYY4Qh8AAJAknb7gku7lUW0UaTarb1QbSdJZp0PHKsqDVRr8gNAHAAAkScmtotz/3mg/pU25uXqr4Ix7WW6lsUJffl6uVi5fqvy83GCXEhIMG/oyMjKUmpqquXPn1lqXlZWlSZMmaciQIRo2bJimTp2qAwcO1Nru/fff19ixY9WvXz+NHj1av//97+V0OpujfAAAQs6lkVH6RXRbSdI/S4s1+eBBlV1wSTfCZApWaR7l5+dp9YrnlJ+fF+xSQoLh7t612+2aP3++9u/fr6ioqFrr9+3bp7S0NP3617/Wk08+qdLSUj333HNKS0vTtm3bFB8fL0n68MMP9fDDD+vRRx/Vz3/+c2VlZenJJ59URESEpk+f3tynBQBASHg+4TL99oeTervgRxW5fuooMUlKadU6KDUdzclWYWFhreXZhw/V+H6xmJgYdUvuHtDaQonhQt+2bdtUXFysrVu36rbbbqu1PiMjQ507d1Z6errM5qqOysWLF2vMmDHavn27Jk+eLEn63e9+p4kTJ+rOO++UJHXp0kXt27dXu3btmu9kAAAIMf8sLVKvqDZ6Pa6jTllNmvb11yp3udQ/yqpLLM0fG47mZOsX11xV7zbzZk+rc92O3VkEv/MMF/pGjRqlCRMmyGLxPBbQs88+q5KSEnfgk6SEhARJUnFxsSTpwIED+vbbb3XTTTfVOjYAAKjb86e/1/6yklrLH+zQOQjVyN3Dt3HTJvXp07fR+x08eECTJ03y2EMYrgwX+rp27VrveqvVKqvVWmPZrl27JEkDBgyQpBqf77vvvvv0r3/9S23bttUdd9yhSZMmyWSwzyQAAGAUV7WOrhH6EiMi9VjHJI20xgaxKqlPn74aNGhQUGsIdYYLfd46fvy4Fi1apJEjR2rYsGGSpB9++EGS9NRTT+nee+/VnDlz9Oc//1np6emqrKzUlClT6jyeyVT1FQqq6wyVesMJbWNMtIsx0S7G8pSti6ZfkqCcilL1sLVTXIFDFjW+cdztaDr/1aidftr34tdBU18XofR3vTFM9TxXDQnp0Hf48GHdc889stlsWrZsmXt5ZWXVOEN33nmnbrzxRknS5ZdfrsOHD2vt2rVKS0ur8/JxcmKcoqNDa47B5MS4YJeAOtA2xkS7GBPtYhwpkq6u/qGtd/va86p6BC1mkyzmxqUS5/ntuthilZJU83VQfbwIi1kRlsYPOlK9radjtgTJiXEqKor0ap+QDX1ZWVmaMWOGevTooZdffrnGDRqxsVUvkNTU1Br7XHXVVfrwww/1/fffq0uXLh6Pm3PSLqu1InCF+5HJVNXoOSftYpB0Y6FtjIl2MSbaxZh8bZfj+QWSJIfTJbOzcTs6zm93PL9AcSfsHo9X6XCq0tH4Ydeqt/V0zFB2YbsUFRV5tW9Ihr4vvvhCU6dO1YgRI7R8+XK1atWqxvpu3bpJks6ePVtjefX0MTExMXUe2+VSyL3phGLN4YK2MSbaxZhoF2Pytl3c27rOfzVqp7ofq6mviZb6uvLlvAw7OHNdTp8+rfvuu08jRozQypUrawU+qapHz2q16s9//nON5VlZWUpISFBcXMvr5gUAAKiP4Xr67Ha7KiqqLq86HA6VlZXp1KlTkqou265cuVLl5eV6+OGHdebMmRr7RkZGKi4uTm3atNH999+vF154QcnJyRo5cqQ+/PBD7dy5UwsWLGj2cwIAAAg2w4W+WbNmae/eve6fc3NztXPnTklSenq69uzZo4KCAo0ZM6bWvkOGDFFmZqYkadq0aYqKilJGRobS09PVuXNnPfPMM7r11lub50QAAAAMxHChrzq01WX8+PGNPtbkyZPdM3QAAACEM8OFPgAAgIsdPHig4Y2asH04IPQBAADDqh5xY/KkSU3aH4Q+AABgYN2Su2vH7iyPc+hmHz6kebOnadmqtereo1et9TExMeqW3L05ygwJhD4AAGBoDQW37j16KbVf/2aqJnQR+gAAgN85HJUB2Ra+I/QBAAC/qf4M3blzZxvYsu59ERiEPgAA4Df1fQavPnz+LvAIfQAAwK8Ib8YUcnPvAgAAwHuEPgAAgDBA6AMAAAgDhD4AAIAwQOgDAAAIA4Q+AACAMEDoAwAACAOEPgAAgDBA6AMAAAgDhD4AAIAwQOgDAAAIA4Q+AACAMEDoAwAACAOEPgAAgDBA6AMAAAgDhD4AAIAwQOgDAAAIA4Q+AAAASfl5uVq5fKny83KDXUpAEPoAAAAk5efnafWK55SfnxfsUgKC0AcAABAGCH0AAABhICLYBQAAADS3oznZKiwsrLEs+/ChGt8vFhMTo27J3QNeW6AQ+gAAQFg5mpOtX1xzVZ3r582eVue6HbuzQjb4EfoAAEBYqe7ha9u2nSyWxkUhh6NS586drdU7GEoIfQAAICxZLBGKjIwMdhnNhhs5AAAAwoBhQ19GRoZSU1M1d+7cWuuysrI0adIkDRkyRMOGDdPUqVN14MCBOo917Ngx9e/fX3fddVcgSwYAADAsw4U+u92u6dOna/369YqKiqq1ft++fUpLS1Pnzp21efNmrV27ViUlJUpLS9OpU6dqbe9yufTEE0+osrKyOcoHAAAwJMOFvm3btqm4uFhbt25Vu3btaq3PyMhQ586dlZ6erp49e6pfv35avHix7Ha7tm/fXmv71157TTk5Obruuuuao3wAAABDMtyNHKNGjdKECRNksVg8rn/22WdVUlIis/mnvJqQkCBJKi4urrFtbm6ufve732nJkiX6+OOP9eOPPwaucAAAAAMzXE9f165d6wx8kmS1WtWhQ4cay3bt2iVJGjBgQI3lCxYs0NChQ3XDDTf4v1AAAIAQYriePm8dP35cixYt0siRIzVs2DD38nfeeUdZWVkeL/nWx2Sq+goF1XWGSr3hhLYxJtrFmGgXYwqFdrmwRm/qdG9rOv/VqJ18eyx/8/WcpRAPfYcPH9Y999wjm82mZcuWuZefOXNGS5Ys0cMPP+y+9NtYyYlxio6O9nepAZWcGBfsElAH2saYaBdjol2MycjtYs+LlSR1scUqJanxdVbvZzGbZDE3Ljk5z2/n7WMFSnJinIqKvBtjMGRDX1ZWlmbMmKEePXro5ZdfrnHTxzPPPKM+ffro9ttv9/q4OSftslor/FlqwJhMVY2ec9IulyvY1eBCtI0x0S7GRLsYUyi0y/H8Avf3uBN2r/dzOF0yOxt3co7z23n7WP52YbsUFRV5tW9Ihr4vvvhCU6dO1YgRI7R8+XK1atWqxvrt27fLbDbriiuucC9zOp1yuVy6/PLL9eyzz+qWW27xeGyXS4Z9cdclFGsOF7SNMdEuxkS7GJOR26W6Lm9rdG/rOv/VqJ18e6xA8aWOkAt9p0+f1n333acRI0Zo5cqVHm/6ePfdd2ste+GFF5SXl6f09HR16tSpOUoFAAAwDMOFPrvdroqKqsurDodDZWVl7kGXY2NjtXLlSpWXl+vhhx/WmTNnauwbGRmpuLg49erVq9Zx27Ztq4KCAo/rAAAAWjrDhb5Zs2Zp79697p9zc3O1c+dOSVJ6err27NmjgoICjRkzpta+Q4YMUWZmZrPVCgAAECoMF/oaCm3jx4/36bhLly71aT8AAICWwHCDMwMAAMD/CH0AAABhgNAHAAAQBgh9AAAAYYDQBwAAQpLNlqBZcx+RzebdlKvhynB37wIAADSGLaGT5jw0P9hlhAx6+gAAAMIAoQ8AACAMEPoAAADCAKEPAAAgDBD6AAAAwgChDwAAIAwQ+gAAAMIAoQ8AACAMEPoAAADCAKEPAAAgDBD6AAAAwgChDwAAIAwQ+gAAAMIAoQ8AACAMEPoAAADCAKEPAAAgDBD6AAAAwgChDwAAIAwQ+gAAAMIAoQ8AACAMEPoAAADCAKEPAACEPqdT0e9vU8K9d8hy8niwqzGkiGAXAAAA4DOnU9EfbFf755co6tBBSVLBb+5QcWKXIBdmPIQ+AAAQei4Key4zFy8bQugDAACho46wZ3I6g1yY8RH6AACA8blciv6f9wh7TUDoAwAAhtf6739Vp2l3yXX+Z8Ke97gADgAADK908NX64cnFcsa1l8tkcoc/NJ5hQ19GRoZSU1M1d+7cWuuysrI0adIkDRkyRMOGDdPUqVN14MCBGtucOXNGCxYs0OjRozVgwADddNNNeu2115qrfAAA4E8RETo77QEd++wLnX58EeHPB4YLfXa7XdOnT9f69esVFRVVa/2+ffuUlpamzp07a/PmzVq7dq1KSkqUlpamU6dOSZLKy8t177336m9/+5sWLVqkt99+W7/61a+0YMECvf76VYfeoQAAIABJREFU6819SgAAwE9c1midvW8m4c8Hhgt927ZtU3FxsbZu3ap27drVWp+RkaHOnTsrPT1dPXv2VL9+/bR48WLZ7XZt375dkvS3v/1N+/fv1+LFizVy5Ehddtlluv/++zVw4EC9+uqrzX1KAADAzzyGv2AXZXCGu5Fj1KhRmjBhgiwWi8f1zz77rEpKSmS+YDyehIQESVJxcbEkacSIEdq9e7fi4+Nr7JuQkFDrMjAAAAhd1eHv3F13q23mBsW+8aoquvcMdlmGZLjQ17Vr13rXW61WWa3WGst27dolSRowYIAkKSIiwh0EqxUWFmrv3r0aOXJkvcc3maq+QkF1naFSbzihbYyJdjEm2sWYQq5doqN1bvpMnZs+U5JUX9nuczI1sGGNnX7aN5jPiakJdRgu9Hnr+PHjWrRokUaOHKlhw4Z53MbpdOrxxx9XaWmpZsyYUe/xkhPjFB0dHYhSAyY5MS7YJaAOtI0x0S7GRLsYU0tsF3terCTJYjbJYm5ccnKe366LLVYpScF/TpIT41RUFOnVPiEd+g4fPqx77rlHNptNy5Yt87hNeXm5HnnkEe3cuVO///3vlZycXO8xc07aZbVWBKJcvzOZqho956RdLj7IYCi0jTHRLsZEuxhTS26X4/kFkiSH0yWzs3En5zi/3fH8AsWdsAestoZc2C5FRUVe7RuyoS8rK0szZsxQjx499PLLL3u86aOwsFAPPPCAvvzyS61bt67OnsALuVwKuRd3KNYcLmgbY6JdjIl2MaaW2C7u83Gd/2rUTj/ta4Tnw5c6QjL0ffHFF5o6dapGjBih5cuXq1WrVrW2KS8v1/3336+cnBxt3rxZffr0CUKlAAAAxhByoe/06dO67777NGLECK1cubLOu3wXLFigw4cP69VXX23wki4AAEBLZ7jQZ7fbVVFR9Zk6h8OhsrIy96DLsbGxWrlypcrLy/Xwww/rzJkzNfaNjIxUXFyc9u3bpz/96U968sknFRMT496/2iWXXFJnWAQAAGiJDBf6Zs2apb1797p/zs3N1c6dOyVJ6enp2rNnjwoKCjRmzJha+w4ZMkSZmZnas2ePJOmZZ57RM888U2u7nTt3qkuXLgE6AwAAAOMxXOjLzMysd/348eMbPMbMmTM1c+ZMf5UEAAAQ8gw3DRsAAAD8j9AHAAAQBgh9AAAAYYDQBwAAEAYIfQAAAGGA0AcAABAGCH0AAABhgNAHAAAQBgh9AAAAYYDQBwAAEAYIfQAAAGGA0AcAABAGCH0AAABhgNAHAAAQBgh9AAAAYYDQBwAAEAYIfQAAAGGA0AcAABAGCH0AAABhgNAHAAAQBgh9AAAAYYDQBwAAEAYIfQAAAGGA0AcAABAGCH0AAABhgNAHAAAQBgh9AAAAYYDQBwAAEAYIfQAAAGGA0AcAABAGCH0AAABhgNAHAAAQBiKCXQAAAEAwOByVAdnWqAh9AAAgrMTExEiSzp076/O+ociwoS8jI0PPP/+8rr/+eq1YsaLGuqysLK1atUoHDx6UxWJRamqqHnroIfXt29e9zblz5/Tcc89p165dKiwsVI8ePTR79mxde+21zX0qAADAQLold9eO3VkqLCyssTz78CHNmz1Ny1atVfcevWrtFxMTo27J3ZurTL8zXOiz2+2aP3++9u/fr6ioqFrr9+3bp7S0NP3617/Wk08+qdLSUj333HNKS0vTtm3bFB8fL0maNWuWTpw4oRdeeEEdO3bUO/9/e/ceFmWd/3/8OScOo3KSQGAQOQ4KqKhoiZgHkq7MPLTVuuVVdtZdO23XtWW7m5QruxWl0pXXXqGSgrUdVpbS1jy0kQbSEiu0ZVpqHtlVjGSGwzAz9/cPfzM/WNOylPuGeT+uq+vSGWau9+2ruefN5/58PndFBb/85S9Zv349o0eP7unDEkIIIYSGXKh5S0xKIT1jRA9W0zM0t5DjnXfeobW1lfLycoKDg895vqSkhKioKAoKCkhOTiYjI4OlS5fS3NzM5s2bAfj444+prq5myZIljBs3jsTERB5++GEyMjJ46aWXevqQhBBCCCFUp7mRvquvvpq5c+diMBi+8/lly5bR1taGXv//+9XIyEgAWltbAdi5cycBAQFceeWV3V6bk5PDqlWrcDgc+Pn5XaYjEEIIIYTQHs01fbGxsRd83mw2Yzabuz22Y8cOAEaOHAnAwYMHiYqKwmjsfnhxcXE4nU4OHz5MUlLSd76/Tnf2v97AU2dvqdeXSDbaJLlok+SiTb6YS2RkJA88/BsiIyM1e9xdc7nYGjXX9F2so0eP8tRTTzFhwgSuuuoqAGw2G/369TvnZz0rblpaWs77fvHRId/5Wi2Ljw5RuwRxHpKNNkku2iS5aJMv5ZIQE8KVo/6odhk/SHx0CHa76aJe06ubvi+//JI777yTiIgICgsLL8l7HjzejNnceUne63LT6c6GfvB4M4qidjWiK8lGmyQXbZJctEly0aauudjt9ot6ba9t+v75z3+ycOFCkpKSWLVqVbdFHwMGDODYsWPnvMYzwhcUFHTe91UUet3/3L2xZl8h2WiT5KJNkos2SS7a9GNy0dzq3R+ioaGBe+65h7Fjx1JSUnLOKt+EhAROnDhBZ2f3EbtDhw5hMpkYPHhwT5YrhBBCCKG6Xtf0NTU1cd9995Gdnc2KFSu+cxXupEmT6Ojo4KOPPur2+Pbt28nJycFkurhr4EIIIYQQvZ3mLu82Nzd7R+hcLhcdHR2cPHkSOHvZdsWKFTgcDh599FFOnz7d7bUmk4mQkBBGjBjB5MmTyc/Pp6CggOjoaEpLS/nqq69YtmxZjx+TEEIIIYTaNNf0LVq0iJqaGu/fGxsb2b59OwAFBQXs3LmTlpYW8vLyznnt2LFjWb9+PQCFhYU888wzPPTQQ9hsNoYOHcrq1atJS0vrmQMRQgghhNAQzTV9nqbtfObMmfOD3qdfv37k5+eTn59/KcoSQgghhOjVet2cPiGEEEIIcfGk6RNCCCGE8AHS9AkhhBBC+ABp+oQQQgghfIA0fUIIIYQQPkCaPiGEEEIIHyBNnxBCCCGED9DcPn1qULrcsbi1tVXFSi6OTgd2u4nWVrvcDFtjJBttkly0SXLRJslFm7rm0rVnUX5ASNL00b3RuzIzRcVKhBBCCCEuXmtrK/3797/gz8jlXSGEEEIIH6BTfsh4YB/ndrs5deoUAGazGZ1Op3JFQgghhBAXpiiK92pleHg4ev2Fx/Kk6RNCCCGE8AFyeVcIIYQQwgdI0yeEEEII4QOk6RNCCCGE8AHS9PkAmbapTZKLED9MS0sLb7/9ttplCNHryT59fZjL5cJgMKDT6XC73d+7qkf0DMlFm9ra2ti4cSPHjh0jOjqa3NxcIiMj1S7L59lsNnJzcxk9ejQzZsxQuxxxHjabDX9/f0wmk9qliAswLFmyZInaRYhLr7W1lYULF+JwOEhLS/M2GLIdjbokF22y2WzcdNNN7N+/n0OHDlFeXs6RI0fIycnB399f7fJ8ls1m4/rrr2fcuHGsXLlS7XLEeTQ0NLBo0SKio6OxWCwYDAa1SxLnISN9fdRrr73Grl27OH78OH5+fsycORO9Xi8jSyqTXLTH4XBw9913Y7VaKSgowO12s3v3bhYsWMCsWbPIzc1Vu0Sf5Gn4hg8fzvPPPw+A0+nEaJSvLa1QFAWdTsebb77J3r17+dOf/oTRaGT8+PHS+GmUfMv0UUeOHCEuLo7AwEBWrlzJ3/72NwBvgyHUIbloT319PS6Xi0ceeYSAgABMJhNZWVkMGzYMs9nMmTNn1C7R53R0dDB37lyioqK8I3wOh8Pb8J0+fZr9+/djt9vVLNPnea5QfP3118ycOZOIiAgWL15MVVUVTqdT5erEd5Gmr49RFIXOzk6OHz/O/fffz1NPPUVQUBBFRUXSYKjI7XZLLhr17bff0tDQwMmTJwEwmUz079+f5uZmli9fzjXXXMO9997Lpk2bVK7Ud9TV1eF0OvHz8+Pf//43AH5+fjidTh588EHmzZvHjBkzmDt3LsXFxSpX69scDgdut5upU6fy9NNPexu/6upqafw0SOb09TE6nQ6DwcDhw4cJDw8nOzubuLg4amtr+fDDDwkODiY1NVXmkvUwyUW7bDab95K72WxGr9czf/58+vfvz6xZs7jmmmt4++23qaurIzExkdjYWLVL7vMsFgthYWHU19fz4Ycfkp6eTnh4OHfeeSft7e3Mnj2b2bNnU1dXR1VVFZGRkSQnJ6tdts/xTEupqqoiOzsbq9XKqFGj2L17NxUVFVitVqKjo9Hr9d5LwZ7XyTlOHdL09QFutxuXy0VnZ6f38kd8fDypqakYjUZiY2MZPHjwdzYYcHZoPiQkRM1D6NO6nuDi4+MZNmwYBoNBclGZJ5fIyEjsdju7d+9mw4YN7N69m7a2NoqLixk3bhxWq5WJEyeyZs0aAgICmDhxotql90lOpxObzcapU6cICgoiOTmZwMBA6uvrqays5IsvvqBfv34sWbKEsWPHkpSUxFVXXcVrr72Gv78/kydPVvsQfILNZsPhcKAoCiaTCZ1Ox9SpU4mIiECv1zNw4EAyMzO9jV9KSgrR0dHeOX4nT56kf//+Kh+F75Kmr5ez2Ww8+eSTrFmzhs2bN7Nv3z5GjBhBaGgoRqMRl8uFXq/v1mBUVlYSEhJCamoqK1asYOPGjUyaNAk/Pz+1D6fPaG9vp7a2FovFgk6n8+bQr1+/bhOcJZee9b+5dHZ2YjAYyMrKYtKkSdx6660cPXqUqKgo7/YgLpeL8PBw6uvrOXXqFNdff70surnEbDYbTzzxBMXFxbz66qt88cUX5ObmkpycTEBAAJ9//jm7du1i4sSJZGdnez9ToaGh1NTUcObMGWbMmCGjR5dZXV0dixcvZsOGDfz5z3/m5MmT9OvXzzua5/lF6n8bv7S0NKKiorj11lvZvHkzP/vZz9Q+FJ8lTV8vZrfbmT17NmazmTFjxmAymSgvL2fHjh1cccUVWCwWTCZTt8YvNjaWTz75hOrqaiorK/nrX//Ks88+K5esLiGHw8EvfvELtm7dyqBBg0hISOh2QvTwXO6QXHrGd+ViMBi8jV9QUBDBwcFs2bKF5uZmJk2ahL+/PzqdDp1Ox8aNG0lKSiInJ0eai0vIbrdz0003ERwczJQpUwgLC2Pjxo189dVX5OXlkZKS4t02Z/78+QwYMAA4OwfW6XRSXl5OWloa48ePV/Mw+rz6+npuv/12pk6dyvTp0wkNDWXLli289957REZGkpSU1G16ysCBAxkxYgS1tbVUVFRQXl5OR0cHr732mqzsVZMieiW3260UFBQod999t+JwOBRFURSn06nU19cr1157rTJhwgRl48aNSkdHh/c5j7q6OiUjI0PJyspSPv/8c1Xq78saGxuV7OxsJT09XZk+fbqyY8cO73Nut7vbz3b9u+RyeV0oF5fL5f1zUVGRkpqaqqxevVo5duyY0tzcrBQWFipZWVnKl19+qUbpfZbb7VYKCwuV++67z3uustvtSlFRkZKRkaF88skn3p9tb29XFEVRbDab0tLSorS2tirPP/+8Mm7cOOXAgQOq1O8rHA6HsmjRIuW3v/1tt8e3b9+uzJs3T0lNTVUqKiq8j3f9PO3bt0+xWq3KLbfc4v2u6uzs7JnCxTnkGkUvpdPpOHToEMHBwd4d0A0GAxkZGWzYsIHQ0FCee+45KisrcbvdGAwG722/ampqMBgMlJWVkZqaquZh9En79u0jPDyc0tJS/vOf/1BYWMiOHTuAs7kpXW6/1vXvksvldaFc9Ho9LpcLgF/96lfk5eXxzDPPMGfOHO666y7Ky8spKSkhMTFRzUPoc3Q6Hfv37ycoKMg7jcFsNjNlyhScTqd3RTWAv78/TU1NPPfcc9x2223ce++9lJeXs2bNGuLj49U6BJ+gKApHjx713qHG4XAAMGXKFB555BEmTJjA448/ztatWwG6TX8oLCwkMTGR0tJSTCaT7LWoMmn6eiFFUXA6nZjNZr755htsNpv3ObfbTWhoKOvWrfNuCdLc3AycPcH+5S9/4cUXX2TdunWy2u0yMZlMBAQEMGLECEpKSjhx4sQ5jV/XrVkkl57xfbl4LvUCLF++nD/+8Y/Mnz+fOXPmsGHDBoYNG6Zm+X2Ooig4HA6+/fZbmpqa6Ojo8H4uBg8eTEREhPfc5Xl84MCBJCYmkp6ezpQpUygtLZVceoCfnx9BQUFUV1d7/+7ZjmXkyJEsXLiQkSNHsmLFCvbu3et93ZIlS6irq6O8vByj0SgNnwbInL5eSKfTodfraW9vZ82aNSQnJ2O1Wr3L591uN4GBgYwaNYp169Zht9vJyckBoLGxkQULFshI0mXU0dFBVFQUVquViIgIJk2axCuvvEJDQwNRUVHEx8efszXLf//7X+677z7J5TL6IbkYDAYcDgcGg4HU1FTGjBlDRkYGQUFBapff53i2MWpvbyc6OpqsrCzv58Fms1FaWsrVV19NamqqdzRcp9MxfPhwpkyZQmZmJsHBwWoegk9Q/t/cY7vdzrZt2wDIzMz0zqnU6/VERUURGBjIu+++S3R0NCNGjAAgMTGRBx98UEb4NESavl4sOTmZgwcPsnr1arKysoiJifE2foqiEBYWxsmTJ2loaODaa6/Fz8+PhIQEQkND1S69TwsLC/M2EJ2dnRds/MrLywkMDCQzM5OwsDC1S+/TfmguBoOB8vJyTCYTYWFh3fYXE5fe8OHDyczM7PbYmTNnWL16NdOmTSM5Odm7mOaNN97g9OnTxMXFSS49xPNvHBsby65du6iqqmLgwIGkpKR0a/ySk5P59NNPqamp4ZZbbkFRFIKDg71TJ6Th0wa5vNuLGQwG5s+fz9ChQ7nnnnuora3ttgmmwWAgJiYGm80mq6V6mGd+kmf1tNVqpaysjMbGRgoLC6mqqmLlypU89thj8sXVgy4mF89cWcmn5zkcDvz8/Lr9grpixQp+97vfYbFYAMmlJ3mmDRUWFuJyuVi+fDkVFRUAGI1GOjo6gLONoWfeX9d85PtHO6Tp07j29nZOnDhx3ueHDx/OQw89REpKCnfddRebN2+mra3N+/zx48eJi4uTE+Ql9n25dGUwGHC5XKSmplJWVsapU6e4++67KSkp4c0332Tw4MGXuVrfIblo08Xk4mEwGAgICADghRdeYO3atbz55puyaEMFntG6qKgoysrKcLvdLF++nJdeegnAu6VOQ0MDERERapYqvodO6bqUUGiK2+1mwYIFNDQ08OqrrxIXF9ft+a6XN/bs2cPq1at57733yM3N5YorrsDlcvHOO++wYcMGmSt2CX1fLufjcrkwGAw88cQTbNu2jdLSUlm0cQlJLtr0Y3JpamoiLy+P/Px8jh07RlFREa+++irp6ek9ULFv83wePLp+z3iea2xs5IknnmDv3r0EBQUxbNgwjh49SktLCxUVFRiNRrn8rlEyp0/DXC4XgYGB1NbWsmXLFsaPH9/ttlye7T50Oh2DBg1i6tSpxMbGcuDAAY4ePcqAAQP4wx/+gNVqVfEo+p7vy+V89Ho9zz//PK+//jqlpaXSiF9ikos2XWwuiqLgcrn44IMPePvtt6mpqaG0tJSMjIwerNo3KYqCXq+no6PDO8+16/eMZ8QvKCiIyZMnY7VaaW1txWQyYbVaeeGFF7yrdOWSrkb15KaA4uI5HA7l/fffVyZPnqxMnz5dOXjw4Pe+xrPJqWcjTHHp/ZhcDh48qOTk5Cj19fWXv0AfJblo04/JpaioSBk5cqRsVN5DPBv4u91u5d5771Vmzpyp7Ny50/t8143ku26+fL73Edokl3c1quvydkVRqKysJD8/H7PZzIsvvsiQIUPO+1pFhtUvm5+SC5zdikJuNn7pSS7a9FNyaWpqQlEUwsPDe6ha3+XJqa2tjQMHDrBixQr27NlDYmIiDzzwAFdeeSVw7neLfNf0PnJ5V6M8O5q/9dZbBAUFMXz4cIYMGcK2bdvYunXrBS+RyIfw8vmxuXhOjiaTSfK5DCQXbfop5zGz2YzZbO7Jcn2SoigYDAZsNhtz5szh8OHDhISEYLFY+OCDDzh06BAxMTHExsZ2u9QL8l3TG0nTp2GVlZU8/PDDtLW1kZaWRlpa2g8+YYrL58fkIifJy09y0SY5j2mbTqfD5XKxePFiOjs7WbVqFdOmTWPatGkMGTKEN954gy+//BKLxYLFYjmn8RO9izR9GhYXF0dgYCDFxcW0tLQwbNgw0tPTu50ws7Oz5YTZwyQXbZJctEly0T673c769etJTU0lNzcXp9OJTqfDarUSGxvLunXrOHz4MJGRkQwePFgavl5Mmj4NUBTFeycND88u56NGjSIgIIDi4mJsNlu3E+Y//vEPXn/9dXJzc+V2RJeB5KJNkos2SS69hycXD39/f7Zt28ann37K7NmzCQgIwOVyodPpSElJYd++fezZs4ejR49isViIjo5WsXrxU0jTpyLPB8+zFB7ObkI6aNAgBg4ciMvl8p4wPb8p2+12hg4dSnp6OlFRUdTX1zN9+nQ5WV5Ckos2SS7aJLn0Pnq9ntbWVl588UViY2MJCgqira2NyspKTpw4QWZmJmaz2Xt/8OrqaiwWC1VVVbS1tTF16lTvrfFE7yJNn0psNhs33HADLS0tZGVlAVBVVcXSpUvZuXMnEydOJCQkpNsJs7Ozk/Xr1+N2uxkyZAiZmZnMnDlTVrddQpKLNkku2iS59F7vv/8+S5cu5fTp02RlZTFmzBgOHz7Mli1bOH36NGPGjCEgIACHw8GmTZt47LHHGDRoEC+//DI33HCD3MO9l5KmTwU2m42ZM2fS1NTErl278Pf3Z/To0cTGxhIcHExtbS2bN28mJyeH0NBQ72/S4eHhbN26laqqKhRFITs723v7G/HTSS7aJLlok+TSu1ksFmJiYli7di2HDx8mOzubvLw8Dhw4wLvvvktFRQVff/01RUVFfPPNN9xxxx20t7fzySefMGvWLBmV7aWk6ethbrebxYsXY7FYKCwsxG63s2bNGoxGI2PGjCEjIwOj0UhNTQ1///vfmTBhAmFhYQC0traiKAo333wzU6ZMkd+MLyHJRZskF22SXHoXz0ir53ItgNFoJDk5mSuuuIJXXnmFQ4cOMWHCBK677jrMZjONjY189dVXpKSkUFxcjMFgoKysjKamJm6++WbvfZFF7yJNXw/T6XSkpaVx4403EhISQnx8PK2trZSUlHQ7YZpMJj7++GM2bdrE+PHjCQwMpKysjD179vDrX/9aTpSXmOSiTZKLNkkuvYter6etrY3f/OY3DBgwgMGDBwNgMBhISUkhIiKCkpISjhw5QlZWFuPGjWPGjBnMmjWL8ePH89577/HKK6+wadMmioqKsFgsKh+R+LGk6VNBUFCQ989hYWEkJCRgt9vPOWH6+/tTV1fHqlWr2L59O1VVVTz33HNERkaqWH3fJblok+SiTZKLtv3vXnoffvghxcXFfPbZZyQkJBATEwOcbfwSEhLQ6/WUlZVhs9lISkoiNDQUPz8/iouLeemllwgNDeWZZ56Re1P3ctL0aUBoaGi3E6afnx+jR48mPT0dq9WKxWIhIiKC/Px8kpOT1S7XZ0gu2iS5aJPkog3t7e0YjUZvw+dp/uLj44mMjOSjjz6iqqqqW+NnMpkwGo1UVlby8ccf069fP++t11JTU5k3bx7XX3+9NOp9gDR9GtH1hLl27VosFgupqanExMQwduxYsrOzvXNiRM+RXLRJctEmyUVdDQ0N5Ofn43Q6SUhI8DZ/nZ2dGAwGUlNT6d+/P1VVVVRXVxMfH++9VNvc3Iyfnx+PP/441113nXf7nYCAAAICArz3UBa9mzR9GhIaGkp8fDz79++nsbGRyZMnd9v7SvZEUofkok2SizZJLuqw2+08/vjjVFVVsX37dnbt2sWRI0dIT08nICDA++8+dOhQzGYz1dXV7Ny5k4iICMxmM88++ywdHR3cdttt6PX6czZwFn2DJKoRbrcbgMTERIYOHcrx48cxmUwYDAZATpRqkVy0SXLRJslFPWazGavVSkpKCm+88QZJSUls2rSJ6667jsLCQv71r395f/bGG29kwYIFGI1GFi1axM0330xjYyPLli3z/oyM7PVNkqpGnDp1ioiICAA6OzuJiorC6XR6T5ZCHZKLNkku2iS5qMMzb+/+++9n48aN1NfXU1BQwIkTJ1i9ejV79+5l7dq1zJs3j7S0NG644QZmz57N0KFD+eyzz2htbWXu3LkYDAacTqc0fH2YTlEURe0ifF1NTQ0LFy4kLS3Nu9LNc/NroR7JRZskF22SXNTldrtxOp0sXbqUAwcOsHLlSu/8yZdffpnCwkKSkpL45ptviI+P5/bbb2f48OHdFme4XC5p0Ps4mdOnAW63m/b2dtra2oiNjSU/P5+UlBS1y/J5kos2SS7aJLmoS6fTYTAY8Pf3Z9WqVYwaNYqEhAS2bNnC73//e55++mkWLFhAeno6VVVVrFu3jlOnTjFt2jTvSKHM4ev7ZKRPQ1wul3zwNEhy0SbJRZskF/U98MADmEwmxo4dy5NPPsmjjz7KXXfd1W1O5ebNm8nLy5ORPR8jTZ8QQgjRh5SVlVFQUICiKCxevJif//zn3ubuf+fsySVd3yJNnxBCCNHH3HHHHbS0tPDWW2+pXYrQEBl/F0IIIfoIzzjO5MmT+fbbb6mtre32uPBt0vQJIYQQfYRn3l5ubi5nzpxh27Zt3R4Xvk2aPiGEEKKPiYmJ4Y477mDt2rV89tlnapcjNEJ2YBRCCCH6oLy8POrr67FarWqXIjRCFnIIIYQQfZRnDz5ZpStAmj4hhBCiT/M0fkLInD4hhBCiD5OGT3hI0yeEEEII4QOk6RNCCCGE8AHS9AkhhBBC+ABp+oQQQgghfIA0fUIIIYQQPkCaPiGEEEIIHyBNnxD+NbARAAAAHUlEQVRCCCGED5CmTwghhBDCB0jTJ4QQQgjhA/4Pu5TlZORHY4sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x575 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [0.99*y if ix%2 == 0 else np.nan for ix,y in enumerate(tdf['Low'].values) ]\n",
    "len(y)\n",
    "mks = [m  for m in mymarkers if m is not None]*2\n",
    "len(mks)\n",
    "ap = mpf.make_addplot(y,type='scatter',marker=mks,markersize=45,color=color)\n",
    "mpf.plot(tdf,type='candle',addplot=ap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
